Chapter title |
Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease
|
---|---|
Chapter number | 34 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46720-7_34 |
Pubmed ID | |
Book ISBNs |
978-3-31-946719-1, 978-3-31-946720-7
|
Authors |
Zhengxia Wang, Xiaofeng Zhu, Ehsan Adeli, Yingying Zhu, Chen Zu, Feiping Nie, Dinggang Shen, Guorong Wu, Wang, Zhengxia, Zhu, Xiaofeng, Adeli, Ehsan, Zhu, Yingying, Zu, Chen, Nie, Feiping, Shen, Dinggang, Wu, Guorong, Zhengxia Wang, Xiaofeng Zhu, Ehsan Adeli, Yingying Zhu, Chen Zu, Feiping Nie, Dinggang Shen, Guorong Wu |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
Abstract |
Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue, we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this, our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined subject-wise relationships, and (3) verifies the intrinsic data representation on the training data, in order to guarantee an optimal classification on the new testing data. Furthermore, we extend our pGTL to incorporate multi-modal imaging data, to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal Control (NC) subjects are achieved using MRI and PET data. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 38 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 21% |
Student > Master | 4 | 11% |
Student > Ph. D. Student | 4 | 11% |
Student > Bachelor | 3 | 8% |
Student > Doctoral Student | 2 | 5% |
Other | 9 | 24% |
Unknown | 8 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 6 | 16% |
Computer Science | 5 | 13% |
Psychology | 4 | 11% |
Engineering | 4 | 11% |
Neuroscience | 3 | 8% |
Other | 6 | 16% |
Unknown | 10 | 26% |